Papers by Yaoqing Yang
HTMuon: Improving Muon via Heavy-Tailed Spectral Correction (2026.findings-acl)
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| Challenge: | Muon’s orthogonalized update rule suppresses the emergence of heavy-tailed weight spectra and over-emphasizes the training along noise-dominated directions. |
| Approach: | They propose to preserve Muon's ability to capture parameter interdependencies while producing heavier-tailed updates and inducing heavier-tail weight spectra. |
| Outcome: | The proposed algorithm suppresses the emergence of heavy-tailed weight spectra and over-emphasizes training along noise-dominated directions. |
Model Balancing Helps Low-data Training and Fine-tuning (2024.emnlp-main)
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| Challenge: | Recent advances in foundation models have emphasized the need to align pre-trained models with specialized domains using small, curated datasets. |
| Approach: | They propose a layer-wise learning rate scheduler that balances training quality across layers . they adapt it to a curated dataset to achieve alignment with specialized domains . |
| Outcome: | The proposed model shows that it can be used to balance training quality across layers and improve low-data training and fine-tuning for both NLP and SciML tasks. |
Spectral Insights into Data-Oblivious Critical Layers in Large Language Models (2025.findings-acl)
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| Challenge: | Recent studies have identified critical layers linked to specific functions or behaviors, limiting their use to post-hoc settings. |
| Approach: | They propose a data-oblivious approach to identify intrinsic critical layers in pre-fine-tuned LLMs by analyzing representation dynamics via Centered Kernel Alignment. |
| Outcome: | The proposed approach identifies critical layers in pre-fine-tuned models . layers with significant shifts in representation space are also those most affected during fine-tuning . |
Why LLM Safety Guardrails Collapse After Fine-tuning: A Similarity Analysis Between Alignment and Fine-tuning Datasets (2026.acl-long)
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| Challenge: | Existing mitigation strategies focus on reactively addressing jailbreak incidents after safety guardrails have been compromised. |
| Approach: | They investigate the degradation of safety guardrails through the lens of representation similarity between upstream alignment datasets and downstream fine-tuning tasks. |
| Outcome: | The proposed model reduces harmfulness score by 10.33% when compared to baseline models. |
AlphaLoRA: Assigning LoRA Experts Based on Layer Training Quality (2024.emnlp-main)
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| Challenge: | Recent studies combine LoRA with Mixture-of-Experts (MoE) to improve performance in Large Language Models. |
| Approach: | They propose a method to combine LoRA and Mixture-of-Experts (MoE) to improve performance in Large Language Models. |
| Outcome: | The proposed method reduces redundancy in LoRA experts within the MoE architecture, and improves training quality across layers. |